With the continuous growth of traffic demand and the mismatch of urban transportation facilities, urban traffic congestion has been caused, leading to various related problems, such as environmental pollution, traffic accidents, and slow economic development. Many cities have implemented relevant measures to improve traffic congestion, but fewer are ideal. This study used the hidden Markov model combined with the dissipative structure theory and entropy theory to predict the congestion more accurately. The temporal and spatial distributions of the online ride-hailing Didi data in Chengdu were analyzed. There are morning peaks, noon peaks, and evening peaks during workdays. During the noon peak and evening peak, travel demand in the city’s central area is relatively stable. It is found that the prediction model has a higher accuracy after combining the dissipative structure theory and entropy theory, which could be used to propose methods to prevent congestion.

中文翻译：

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基于耗散结构理论的交通拥堵预测-以成都为例

随着交通需求的不断增长和城市交通设施的不匹配，造成城市交通拥堵，导致各种相关问题，例如环境污染，交通事故，经济发展缓慢。许多城市已经采取了相关措施来改善交通拥堵，但理想城市很少。本研究使用隐马尔可夫模型，结合耗散结构理论和熵理论来更准确地预测拥塞。分析了成都网上叫车滴滴数据的时空分布。工作日有早高峰，中午高峰和傍晚高峰。在中午高峰和傍晚高峰期，城市中心地区的旅行需求相对稳定。